Chunmei Qing

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—Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation.(More)
Slow feature analysis (SFA) extracts slowly varying signals from input data and has been used to model complex cells in the primary visual cortex (V1). It transmits information to both ventral and dorsal pathways to process appearance and motion information, respectively. However, SFA only uses slowly varying features for local feature extraction, because(More)
Seabird populations are considered an important and accessible indicator of the health of marine environments: variations have been linked with climate change and pollution [1]. However, manual monitoring of large populations is labour-intensive, and requires significant investment of time and effort. In this paper, we propose a novel detection system for(More)
Seabird populations are considered an important and accessible indicator of the health of marine environments: variations have been linked with climate change and pollution [9], as well as changes in fish stock levels. However, manual monitoring of large populations is labour-intensive, and so necessarily limited in scope. In this paper we present work(More)
We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition ͑EMD͒ and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function ͑IMF͒ components,(More)