A novel multi-scale fusion framework for detail-preserving low-light image enhancement

Establishing feature correspondences between different images is a key step in many remote sensing applications. In this paper, a novel descriptor based on an extended self-similarity measure, named HOSS (histogram of oriented self-similarity), is proposed for illumination-robust remote sensing image matching. The novel idea of HOSS is a computation of the self-similarity values in multiple directions using an oriented rectangular patch to increase the descriptor distinctiveness. For HOSS construction based on self-similarity values in various directions, a novel index map called RIMC (rotation index of the maximal correlation) incorporated with an adaptive log-polar spatial structure is proposed. Unlike conventional 2D self-similarity descriptor and its extension, HOSS is a 3D histogram of oriented self-similarity values, which is very robust to significant illumination variations. An illumination-robust rotation assignment approach is also developed based on self-similarity gradients to obtain rotation invariance. Experimental analysis on three categories of synthetic and real remote sensing images from various sensors, demonstrate the superior capability of the HOSS over state-of-the-art descriptors, including DOBSS, AB-SIFT, PIIFD, and DAISY, in terms of the recall, precision, and positional accuracy. The average performance of the HOSS in all applied images is about 36%, 51%, and 0.9 pixels in terms of recall, precision, and positional accuracy, respectively. The demo code of the HOSS approach can be downloaded from https://www.researchgate.net/publication/328733655_HOSS_Descriptor .

Illumination-Robust remote sensing image matching based on oriented self-similarity

Robust template matching with large angle localization

Low-light image contains compressed dynamic range that can be enhanced for knowing detail information. Contrast enhancement of low-light image is a challenging task in image processing field. In this paper, we enhance the different type of low-light image by using histogram equalization (HE) and illumination adjustment. We present a method to detect different types of low-light images. Then, we apply the HE on V channel of input low-light image after converting the color space from RGB to HSV. After that, we enhance the contrast of V by adjusting intensity (V) of low-light image with adopting gamma correction. We evaluate our low-light enhanced method by naturalness image quality evaluator (NIQE) and colorfulness-based patch-based contrast quality index (CPCQI) and also compare our proposed method with typical HE method by measuring quality of images.

Contrast enhancement of low-light image using histogram equalization and illumination adjustment

The need for a security improvement of an area generally associated with the assets which are located in that area. The protection mechanism of these assets creates the need for a device which can be used as a monitoring tool. A surveillance camera is a popular device for monitoring an area. For the purposes of data integration, the camera surveillance based on IP (Internet Protocol) is widely used. However, the maintenance of a file server and the provision of the data storage require substantial funding and effort. In addition, if the surveillance activity is carried out in the indoor room, sometimes the lack of lighting in the room can make data retrieval becomes not clearly visible. Thus it creates the need for restoring the data by improving its image quality. This study is going to design a device such as IP surveillance camera which based on an embedded system, with improved image recording feature by using gamma correction and data storage feature which integrates with cloud service through a network based on IP (Internet Protocol). In the image correction mechanism, the measurement of image quality improvement is executed by using a histogram. The measurement of image quality improvement based on the human perspective is also provided.

Design and implementation of surveillance embedded IP camera with improved image quality using gamma correction for surveillance camera

A feature descriptor based on the local patch clustering distribution for illumination-robust image matching

In this paper, a novel methodology is proposed for contrast enhancement and noise reduction in very noisy data with low dynamic range on images captured by surveillance camera under extremely low light condition. For the initial noise reduction, a motion adaptive temporal filtering based on the Kalman filter is employed. Then, the denoised image is first inverted and subsequently dehazed as a tone mapping to enhance the visibility based on the observation that the inverted low light image presents quite similar characteristics to hazy image. Finally, the remaining noise is removed using the Non-local means (NLM) denoising step. The overall approach essentially transforms very dark images progressively into more visible form and effectively reduces the high intensity noise generated by the tone mapping process. From the experimental results, effectiveness of the proposed method is validated by comparing with the most recent and leading conventional method.

Sang Min Yoon

Papers

A feature descriptor based on the local patch clustering distribution for illumination-robust image matching

Image enhancement for extremely low light conditions