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Improving the visual quality of the given degraded observation by correctingexposure level is a fundamental task in the computer vision community. Existingworks commonly lack adaptability towards unknown scenes because of thedata-driven patterns (deep networks) and limited regularization (traditionaloptimization), and they usually need time-consuming inference. These two pointsheavily limit their practicability. In this paper, we establish a PracticalExposure Corrector (PEC) that assembles the characteristics of efficiency andperformance. To be concrete, we rethink the exposure correction to provide alinear solution with exposure-sensitive compensation. Around generating thecompensation, we introduce an exposure adversarial function as the key engineto fully extract valuable information from the observation. By applying thedefined function, we construct a segmented shrinkage iterative scheme togenerate the desired compensation. Its shrinkage nature supplies powerfulsupport for algorithmic stability and robustness. Extensive experimentalevaluations fully reveal the superiority of our proposed PEC. The code isavailable at https://rsliu.tech/PEC.

Practical Exposure Correction: Great Truths Are Always Simple