2017 25th European Signal Processing Conference (EUSIPCO)

Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e., bracketed images) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.

Deep reverse tone mapping

Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image is challenging due to missing details in under-/over-exposed regions caused by quantization and saturation of camera sensors. In contrast to existing learning-based methods, our core idea is to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDR-to-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We then propose to learn three specialized CNNs to reverse these steps. By decomposing the problem into specific sub-tasks, we impose effective physical constraints to facilitate the training of individual sub-networks. Finally, we jointly fine-tune the entire model end-to-end to reduce error accumulation. With extensive quantitative and qualitative experiments on diverse image datasets, we demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.

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Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline

High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end-to-end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.

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ExpandNet : a deep convolutional neural network for high dynamic range expansion from low dynamic range content

Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested.

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An Experiment-Based Review of Low-Light Image Enhancement Methods

The mismatch between the Low Dynamic Range (LDR) content and the High Dynamic Range (HDR) display arouses the research on inverse tone mapping algorithms. In this paper, we present a physiological inverse tone mapping algorithm inspired by the property of the Human Visual System (HVS). It first imitates the retina response and deduce it to be local adaptive; then estimates local adaptation luminance at each point in the image; finally, the LDR image and local luminance are applied to the inversed local retina response to reconstruct the dynamic range of the original scene. The good performance and high-visual quality were validated by operating on 40 test images. Comparison results with several existing inverse tone mapping methods prove the conciseness and efficiency of the proposed algorithm.

Physiological inverse tone mapping based on retina response

Objective
The purpose of this work was to analyze and compare the movement kinematics of sit-to-stand (STS) and back-to-sit (BTS) transfers between frail aged adults and young subjects, as well as to determine the relationship between kinematic changes and functional capacities.

/pdf/kinematic-analysis-of-motor-strategies-in-frail-aged-adults-3hhop9fo5f.pdf

Kinematic analysis of motor strategies in frail aged adults during the Timed Up and Go: how to spot the motor frailty?

Very high resolution (VHR) satellite images provide a mass of detailed information which can be used for urban planning, mapping, security issues, or environmental monitoring. Nevertheless, the processing of this kind of image is timeconsuming, and extracting the needed information from among the huge quantity of data is a real challenge. For some applications such as natural disaster prevention and monitoring (typhoon, flood, bushfire, etc.), the use of fast and effective processing methods is demanded. Furthermore, such methods should be selective in order to extract only the information required to allow an efficient interpretation. For this purpose, we propose a texture region segmentation method using the level set algorithm and the multi-kernel theory. We design a selective and local multi-kernel stop function for which the regularization term depends on the fuzzy membership degree of a given pixel to be on the boundary or not. Favored by its local nature, the method is accelerated by means of an NVIDIA graphics processing unit programming. The new algorithm is selective, effective, and fast. Experimental results on VHR satellite images demonstrate subjectively and objectively the effectiveness of the proposed method.

Multi-Kernel Implicit Curve Evolution for Selected Texture Region Segmentation in VHR Satellite Images

Recent FPGA chips, with their large capacity memory and reconfigurability potential, have opened new ways for rapid prototyping of embedded systems. With the advent of high density FPGAs it is now feasible to implement a high-performance VLIW processor core in an FPGA. In this paper, we describe research results of enabling the DSP TMS320 C6201 model for real-time image processing application, by exploiting FPGA technology. The goals are, firstly, to keep the flexibility of DSP in order to shorten the development cycle, and secondly to use powerful available resources on FPGA to increase real-time performance. We present a modular DSP C6201 VHDL model which contains the hardware just necessary for each target application. The application development cycle proposed is validated with some common algorithms of image. Our results demonstrate that an algorithm can easily be, in an optimal manner, specified and then automatically converted to VHDL language and implemented on an FPGA device with system level software.

A modular VLIW Processor

Increasingly more computing power is demanded for contemporary applications such as multimedia, 3D visualization, and telecommunication. This paper presents a design space exploration (DSE) experience for an embedded VLIW processor that allows finding out the best architecture for given application. The proposed method has been implemented and tested using an image processing chain for direct photo printer. Our results show a considerable improvement in hardware cost and performance. After the best architecture is identified, we applied a technique to optimize the code in VEX system that uses ?inlining? function in order to reduce execution time.

Vincent Brost

Papers

Physiological inverse tone mapping based on retina response

Kinematic analysis of motor strategies in frail aged adults during the Timed Up and Go: how to spot the motor frailty?

Multi-Kernel Implicit Curve Evolution for Selected Texture Region Segmentation in VHR Satellite Images

A modular VLIW Processor

Design Space Exploration for a Custom VLIW Architecture: Direct Photo Printer Hardware Setting Using VEX Compiler