Adaptive image denoising algorithms rely on an error function that measure the distance between an estimated result and expectations. Selection of the error function and its parameters are crucial for a successful denoising implementation. In this paper, a method for determining close-to-optimal parameters for a bell-shaped error function is evaluated. The function with calculated parameters is employed within a gradient optimization algorithm and tested using test images with varying noise types and levels. The restoration results of the denoising test runs that use the proposed parameters are compared against the results of algorithms that employ well-known least squares and sum of absolute differences methods along with a method that combines both. The clear superiority of the bell-shaped error function for the proposed parameters is shown by the test results.