Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.

Specular Reflection Separation Using Dark Channel Prior

Abstract: We present a novel method to separate specular reflection from a single image. Separating an image into diffuse and specular components is an ill-posed problem due to lack of observations. Existing methods rely on a specular-free image to detect and estimate specularity, which however may confuse diffuse pixels with the same hue but a different saturation value as specular pixels. Our method is based on a novel observation that for most natural images the dark channel can provide an approximate specular-free image. We also propose a maximum a posteriori formulation which robustly recovers the specular reflection and chromaticity despite of the hue-saturation ambiguity. We demonstrate the effectiveness of the proposed algorithm on real and synthetic examples. Experimental results show that our method significantly outperforms the state-of-the-art methods in separating specular reflection.

A robust error concealment technique using data hiding for image and video transmission over lossy channels

Abstract: A robust error concealment scheme using data hiding which aims at achieving high perceptual quality of images and video at the end-user despite channel losses is proposed. The scheme involves embedding a low-resolution version of each image or video frame into itself using spread-spectrum watermarking, extracting the embedded watermark from the received video frame, and using it as a reference for reconstruction of the parent image or frame, thus detecting and concealing the transmission errors. Dithering techniques have been used to obtain a binary watermark from the low-resolution version of the image/video frame. Multiple copies of the dithered watermark are embedded in frequencies in a specific range to make it more robust to channel errors. It is shown experimentally that, based on the frequency selection and scaling factor variation, a high-quality watermark can be extracted from a low-quality lossy received image/video frame. Furthermore, the proposed technique is compared to its two-part variant where the low-resolution version is encoded and transmitted as side information instead of embedding it. Simulation results show that the proposed concealment technique using data hiding outperforms existing approaches in improving the perceptual quality, especially in the case of higher loss probabilities.

Apparatus and Methods for Encoding, Decoding and Representing High Dynamic Range Images

Abstract: A data structure defining a high dynamic range image comprises a tone map having a reduced dynamic range and HDR information. The high dynamic range image can be reconstructed from the tone map and the HDR information. The data structure can be backwards compatible with legacy hardware or software viewers. The data structure may comprise a JFIF file having the tone map encoded as a JPEG image with the HDR information in an application extension or comment field of the JFIF file, or a MPEG file having the tone map encoded as a MPEG image with the HDR information in a video or audio channel of the MPEG file. Apparatus and methods for encoding or decoding the data structure may apply pre- or post correction to compensate for lossy encoding of the high dynamic range information.

Image Reconstruction for Hybrid True-Color Micro-CT

Abstract: X-ray micro-CT is an important imaging tool for biomedical researchers Our group has recently proposed a hybrid “true-color” micro-CT system to improve contrast resolution with lower system cost and radiation dose The system incorporates an energy-resolved photon-counting true-color detector into a conventional micro-CT configuration, and can be used for material decomposition In this paper, we demonstrate an interior color-CT image reconstruction algorithm developed for this hybrid true-color micro-CT system A compressive sensing-based statistical interior tomography method is employed to reconstruct each channel in the local spectral imaging chain, where the reconstructed global gray-scale image from the conventional imaging chain served as the initial guess Principal component analysis was used to map the spectral reconstructions into the color space The proposed algorithm was evaluated by numerical simulations, physical phantom experiments, and animal studies The results confirm the merits of the proposed algorithm, and demonstrate the feasibility of the hybrid true-color micro-CT system Additionally, a ``color diffusion'' phenomenon was observed whereby high-quality true-color images are produced not only inside the region of interest, but also in neighboring regions It appears harnessing that this phenomenon could potentially reduce the color detector size for a given ROI, further reducing system cost and radiation dose

Gated Channel Transformation for Visual Recognition

Abstract: In this work, we propose a generally applicable transformation unit for visual recognition with deep convolutional neural networks. This transformation explicitly models channel relationships with explainable control variables. These variables determine the neuron behaviors of competition or cooperation, and they are jointly optimized with the convolutional weight towards more accurate recognition. In Squeeze-and-Excitation (SE) Networks, the channel relationships are implicitly learned by fully connected layers, and the SE block is integrated at the block-level. We instead introduce a channel normalization layer to reduce the number of parameters and computational complexity. This lightweight layer incorporates a simple l2 normalization, enabling our transformation unit applicable to operator-level without much increase of additional parameters. Extensive experiments demonstrate the effectiveness of our unit with clear margins on many vision tasks, i.e., image classification on ImageNet, object detection and instance segmentation on COCO, video classification on Kinetics.