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.

Contrast preserving decolorization

Abstract: Decolorization - the process to transform a color image to a grayscale one - is a basic tool in digital printing, stylized black-and-white photography, and in many single channel image processing applications. In this paper, we propose an optimization approach aiming at maximally preserving the original color contrast. Our main contribution is to alleviate a strict order constraint for color mapping based on human vision system, which enables the employment of a bimodal distribution to constrain spatial pixel difference and allows for automatic selection of suitable gray scale in order to preserve the original contrast. Both the quantitative and qualitative evaluation bears out the effectiveness of the proposed method.

Method for rectifying channel errors in a transmitted image signal encoded by classified vector quantization

Abstract: A novel method for concealing or rectifying channel errors present in a decoded image signal which has been transmitted in a compressed form by using a classified vector quantization technique comprises a step for estimating the class for a current block by detecting boundary edges at each of four groups of neighboring pixels; and a step for estimating the representative vector for the current block by calculating a multiple number of side-matching functions and choosing the candidate vector producing the smallest side-matching function as the representative vector for the current block.

Multiple description image coding using signal decomposition and reconstruction based on lapped orthogonal transforms

Abstract: This paper considers the use of multiple description coding (MDC) for image transmission in communication systems where long burst errors and sometimes complete channel failures are inevitable. A general framework for MDC is proposed, which uses nonhierarchical signal decomposition at the encoder and image reconstruction at the decoder. A realization of this framework using lapped orthogonal transforms (LOTs) is developed. In the encoder, the bitstream generated by a conventional LOT-based image coder is decomposed so that each description consists of a subsampled set of the coded LOT coefficient blocks. In the decoder, instead of using the inverse LOT directly, a novel image reconstruction technique is employed, which makes use of the constraints between adjacent LOT coefficient blocks and the smoothness property of common image signals. To guarantee a satisfactory reconstruction quality, the transform should introduce a desired amount of correlation among adjacent LOT coefficient blocks. The tradeoff between coding efficiency and reconstruction quality obtainable by using different LOT bases is investigated.

Supervised Raw Video Denoising With a Benchmark Dataset on Dynamic Scenes

Abstract: In recent years, the supervised learning strategy for real noisy image denoising has been emerging and has achieved promising results. In contrast, realistic noise removal for raw noisy videos is rarely studied due to the lack of noisy-clean pairs for dynamic scenes. Clean video frames for dynamic scenes cannot be captured with a long-exposure shutter or averaging multi-shots as was done for static images. In this paper, we solve this problem by creating motions for controllable objects, such as toys, and capturing each static moment for multiple times to generate clean video frames. In this way, we construct a dataset with 55 groups of noisy-clean videos with ISO values ranging from 1600 to 25600. To our knowledge, this is the first dynamic video dataset with noisy-clean pairs. Correspondingly, we propose a raw video denoising network (RViDeNet) by exploring the temporal, spatial, and channel correlations of video frames. Since the raw video has Bayer patterns, we pack it into four sub-sequences, i.e RGBG sequences, which are denoised by the proposed RViDeNet separately and finally fused into a clean video. In addition, our network not only outputs a raw denoising result, but also the sRGB result by going through an image signal processing (ISP) module, which enables users to generate the sRGB result with their favourite ISPs. Experimental results demonstrate that our method outperforms state-of-the-art video and raw image denoising algorithms on both indoor and outdoor videos.