Invertible Grayscale with Sparsity Enforcing Priors

Abstract

Color dimensionality reduction is believed as a non-invertible process, as re-colorization results in perceptually noticeable and unrecoverable distortion. In this article, we propose to convert a color image into a grayscale image that can fully recover its original colors, and more importantly, the encoded information is discriminative and sparse, which saves storage capacity. Particularly, we design an invertible deep neural network for color encoding and decoding purposes. This network learns to generate a residual image that encodes color information, and it is then combined with a base grayscale image for color recovering. In this way, the non-differentiable compression process (e.g., JPEG) of the base grayscale image can be integrated into the network in an end-to-end manner. To further reduce the size of the residual image, we present a specific layer to enhance Sparsity Enforcing Priors (SEP), thus leading to negligible storage space. The proposed method allows color embedding on a sparse residual image while keeping a high, 35dB PSNR on average. Extensive experiments demonstrate that the proposed method outperforms state-of-the-arts in terms of image quality and tolerability to compression.

Faithful Extreme Rescaling via Generative Prior Reciprocated Invertible Representations

“…Different from ordinary image restoration works that directly reconstructs an image from its degraded version, invertible image restoration aims to embed significant information into the degraded image to aid the process of restoration. Invertible grayscaling [9,27] proposes a encoder-decoder framework to embed the original color information into a synthesized grayscale image which is further used to recover its corresponding color image. Zhu et al [36] embeds the spatio-temporal information of a video into a "live" image, which can be converted back to a video preview.…”