The challenge of single-image dehazing mainly comes from double uncertainty of scene radiance and scene transmission. Most existing methods focus on restoring the visibility of hazy images and tend to derive a rough estimate of scene transmission. Unlike previous work, in this paper we advocate the significance of accurate transmission estimation and recast our problem as deriving the optimal transmission map directly from the haze model under two scene priors. We introduce theoretic and heuristic bounds of scene transmission to guide the optimum and show that the proposed theoretic bound happens to justify the well-known dark channel prior of haze-free images. With the constraints on the solution space, we then incorporate two scene priors, including locally consistent scene radiance and context-aware scene transmission, to formulate a constrained minimization problem and solve it by quadratic programming. The global optimality is guaranteed. Simulations on synthetic data set quantitatively verify the accuracy and show that the transmission map successfully captures fine-grained depth boundaries. Experimental results on color/gray-level images demonstrate that our method outperforms most state of the arts in terms of both accurate transmission maps and realistic haze-free images.

We propose a scheme called Type-Aware Error Control (TAEC) to provide robust interactive video services over time-varying wireless channels. Basically, TAEC explores the temporal dependence of video frames by pushing out the lower impact frames. In doing so, queuing delay is controlled and the important frame is retransmitted multiple times. We consider the application of the nonstationary stochastic control to further improve the wireless system throughput by exploring the multiuser diversity gain. The techniques involved in our proposed solution consist of constructing a Markov model, reducing the number of parameters estimated, and avoiding the state explosion problem. The provisioning of immediate bandwidth guarantee is another feature of TAEC, i.e., when exploring the multiuser diversity gain, the users share their reserved bandwidth only if the perceived video qualities are acceptable. As shown in the simulation, TAEC improves the perceived video quality in terms of luminance PSNR when compared to other schemes.