Abstract

Contrast enhancement is used for many algorithms in com- puter vision. It is applied either explicitly, such as histogram equalization and tone-curve manipulation, or implicitly via methods that deal with degradation from physical phenomena such as haze, fog or underwater imaging. While contrast enhancement boosts the image appearance, it can unintentionally boost unsightly image artifacts, especially artifacts from JPEG compression. Most JPEG implementations optimize the com- pression in a scene-dependent manner such that low-contrast images ex- hibit few perceivable artifacts even for relatively high-compression fac- tors. After contrast enhancement, however, these artifacts become signif- icantly visible. Although there are numerous approaches targeting JPEG artifact reduction, these are generic in nature and are applied either as pre- or post-processing steps. When applied as pre-processing, existing methods tend to over smooth the image. When applied as post-processing, these are often ineffective at removing the boosted artifacts. To resolve this problem, we propose a framework that suppresses compression artifacts as an integral part of the contrast enhancement procedure. We show that this approach can produce compelling results superior to those obtained by existing JPEG artifacts removal methods for several types of contrast enhancement problems.