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So in my code I implemented Wiener / Tikhonov Regularization (See paramLambda in my code).
The full code is available on my StackExchange Signal Processing Q51460 GitHub Repository.

So in my code I implemented Wiener / Tikhonov Regularization (See paramLambda in my code).
The full code is available on my StackExchange Signal Processing Q51460 GitHub Repository (Look at the SignalProcessing\Q51460 folder).

So in my code I implemented Wiener / Tikhonov Regularization (See paramLambda in my code).
The full code is available on my StackExchange Signal Processing Q51460 GitHub Repository.

So in my code I implemented Wiener / Tikhonov Regularization (See paramLambda in my code).
The full code is available on my StackExchange Signal Processing Q51460 GitHub Repository.

I previously solved similar problems using the Matrix Form so this time we'll solve it in the convolution form.

1. The SNR is not good enough to solve the inverse problem as is. It requires additional regularization (Wiener / Tikhonov (They are the same)).
2. The model of Box Blur doesn't fit.

I previously solved similar problems using the Matrix Form (See my answer to Deconvolution of 1D Signals Blurred by Gaussian Kernel) so this time we'll solve it in the convolution form.

1. The SNR is not good enough to solve the inverse problem as is. It requires additional regularization like Wiener / Tikhonov (They are the same). I implemented it in the code so you have Wiener Filter to play with and understand its working.
2. The model of Box Blur doesn't fit.